Fluid Dynamics
Stretching and Lyapunov Exponents of Polymers in Ultra-Dilute Turbulent Solutions
Demosthenes Kivotides
We analyse bead–spring
polymers coupled to Navier–Stokes
turbulence in ultra–dilute solutions at Weissenberg number \(Wi\approx 80\). The
polymers do not alter the large-scale turbulent structure, but hydrodynamic interactions generate sub–Kolmogorov solvent motion, so the mesoscopic coupling remains two–way. The chains stretch predominantly as material line elements, with measurable deviations caused by the full mesoscopic bead–spring dynamics. Their end-to-end distance exhibits apparent intermediate-range power-law scaling.
Polymer trajectories preferentially sample axisymmetric biaxial extension: the largest extensions and stretching rates occur in high-strain regions, whereas small extensions and relaxation events are concentrated in high-enstrophy regions. The chains align strongly with the intermediate strain-rate eigenvector and avoid the most compressive direction; together with the positive bias of the intermediate strain-rate
eigenvalue, this gives the intermediate direction a significant role in stretching. Vorticity sampled along
polymer paths aligns with both the first and second strain-rate eigenvectors, differing from analogous Eulerian and
vortex-stretching statistics. We also develop a singular-value-decomposition (SVD)-normalised
algorithm for the tangent-flow equations, enabling finite-time Lyapunov numbers to be computed along
polymer trajectories. Their late-time statistics become stable after about ten large-eddy turnover times and, together with ergodic Lyapunov theory, provide estimates of asymptotic stretching rates. The intermediate finite-time exponent is positive for all computed trajectories, with \(E[\lambda_2]/E[\lambda_1]\approx 4/17\), larger than the corresponding material-line value; the strongest dependence occurs between the largest and smallest finite-time exponents.
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Physics and Society
Nonlinear dynamics of information overload: Impact on source localization in complex networks
Ignacy Czajkowski and Robert Paluch
Source localization in complex networks is a rapidly advancing field with numerous real-world applications, including determining the source of misinformation. In this work, we model information spread across several real-world and synthetic complex networks using our Generalized Fractional Susceptible-Infected-Recovered (GFSIR) model, which incorporates the information overload (IOL) phenomenon. Then, we use Pearson's correlation
algorithm to identify information sources in these networks and investigate how information overload affects localization quality. Numerical simulations have shown that localization effectiveness decreases with the parameter $\alpha$, which controls the strength of the IOL, and increases with the spreading rate $\beta$. Our comparison across various
topologies reveals that localization is generally more effective in synthetic structures, with Erdős-Rényi networks exhibiting greater resilience to IOL than Barabási-Albert models. Furthermore, we identified a critical reversal in the impact of network density: while a higher average degree enhances localization when IOL is negligible, less dense networks perform better under strong overload. This phenomenon represents a significant departure from the behavior observed in standard epidemic models.
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Applied Physics · Cross-listed from cond-mat.mes-hall
Formalizing Poisson-Boltzmann Theory for Field-Tunable Nanofluidic Devices
Zhongyuan Zhao, Chudi Qi, Yuheng Li et al. (+3 more)
Nanofluidic devices support unconventional ion transport appealing to energy and information technologies, thanks to the susceptibility of confined electric double layers (EDLs) to various external physical fields. Although experimental studies advance rapidly, the rationalization of field-tunable nanofluidic transport has not reached a formalized and unified level. Here we reformulate the Poisson-Boltzmann theory and reveal distinct EDL regimes on the parameter space. Based on the regime classification, we establish a formal framework for the tunable nanofluidic transport and discuss the electrostatic modulation (ionic transistor) extensively. The framework reproduces the observed conductivity-concentration scaling behaviors, rationalizes the ionic transistors with reconfigurable polarities, and predicts one fundamental thermodynamic limit for electrostatic modulation (60 mV/dec). Being accurate, generalizable and extensible, this framework can account for a wide range of ion transport in confined spaces.
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All authors Zhongyuan Zhao, Chudi Qi, Yuheng Li, Shoushan Fan, Qunqing Li, Yang Wei
Physics and Society
A convex-geometric framework for fully phase-locked states in the finite Kuramoto model
Antonio Garijo, Sergio Gómez and Alex Arenas
We study the finite-size Kuramoto model of all-to-all coupled phase oscillators with heterogeneous natural frequencies and characterize the minimal coupling strength required for the existence of a fully phase-locked
equilibrium (in a co-rotating frame). To remove the degeneracy due to uniform phase shifts, we move to a reduced co-rotating frame and assess stability through the Jacobian of the reduced system: a fully phase-locked state is stable when this Jacobian is negative definite. This defines a stability region in the phase space. The Kuramoto vector field maps this region to a convex set in frequency space, so a fully-locked state at coupling $K$ exists exactly when the rescaled frequency vector $\hat{\mathbf{\omega}}/K$ lies inside that convex image. The critical coupling $K_{\ell}$ is defined as the smallest coupling strength for which a fully phase-locked
equilibrium exists; geometrically, it corresponds to the first intersection of the ray $t\hat{\mathbf{\omega}}$ with the boundary of this convex set. Building on this convex-
geometric structure, we construct an explicit polytope from analytically computable boundary points of the stability region, providing a closed-form upper bound $K_b \ge K_{\ell}$. The bound is exact for frequencies aligned with polytope vertices and offers a fully explicit outer approximation for general frequency vectors. While not uniformly sharp in a quantitative sense, this construction exposes the underlying geometry of stable fully phase-locking solutions. These results provide a practical use the convex-
geometric structure underlying stable fully-locked states in the Kuramoto model.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci
Nonmagnetic-magnetic Transitions in Rutile RuO2
Yue-Fei Hou, Siyuan Liu, Wanxiang Fen et al. (+4 more)
Rutile RuO$_2$ has recently attracted great interest, as its
magnetic ground state remains controversial. Experimental studies have reported either nonmagnetic (NM) or altermagnetic (AM) ground states in different crystalline samples of RuO$_2$, highlighting the need for a reasonable explanation to resolve this contradiction. In this study, density functional theory calculations are performed to reveal the correlation-sensitive and strain-dependent magnetism of bulk RuO$_2$. On one hand, multiple AM phases with different magnitudes of the
spin magnetic moment are identified in the Hubbard parameter space for RuO$_2$. On the other hand, when appropriate strains that significantly change the
crystal cell volume are applied, the ground state of RuO$_2$ can undergo transitions between the NM state (with no
spin splitting) and the
magnetic states (with
spin splitting in the band structure). These findings not only demonstrate intriguing physics in 4\textit{d}-electron-correlated RuO$_2$, but also retain its potential for spintronic applications.
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All authors Yue-Fei Hou, Siyuan Liu, Wanxiang Fen, Jiajun Lu, Xinfeng Chen, Gui-Bin Liu, Ping Zhang
Data Analysis and Statistics · Cross-listed from astro-ph.IM
NOMAI : A real-time photometric classifier for superluminous supernovae identification. A science module for the Fink broker
E. Russeil, R. Lunnan, J. Peloton et al. (+10 more)
Superluminous
supernovae (SLSNe) are one of the most luminous
stellar explosions known, yet they remain poorly understood. Because they are intrinsically rare, efficiently identifying them in the large alert streams produced by modern time-domain surveys is essential for enabling spectroscopic follow-up. We present NOMAI, a machine learning classifier designed to identify SLSN candidates directly from photometric alerts in the ZTF stream, using light curves accumulated over at least 30 days. It does not require any spectroscopic
redshift and is running in real time within the Fink broker. ZTF light curves are transformed into a set of physically motivated features derived primarily from model-fitting procedures using SALT2 and Rainbow, a blackbody-based multi-band fitting framework. These features are used to train an XGBoost classifier on a curated
dataset of labeled ZTF sources constructed using literature samples of SLSNe, along with TNS and internal ZTF labeled sources. The final training
dataset contains 5280 unique sources, including 225 spectroscopically classified SLSNe. On the training sample, the classifier reaches 66% completeness and 58% purity. Deployed within the Fink broker, NOMAI has been running continuously since 18/12/2025 on the ZTF alert stream and publicly reports SLSN candidates every night by automatically posting them to dedicated communication channels. Based on this, we also report the first two-month as an evaluation period, where the classifier successfully recovered 22 of the 24 active SLSNe reported on the Transient Name Server. The achieved performances demonstrate that the classifier provides a valuable tool for experts to efficiently scan the alert stream and identify promising candidates. In the near future, NOMAI is intended to be adapted to operate on the Legacy Survey of Space and Time conducted by the Vera C. Rubin Observatory.
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All authors E. Russeil, R. Lunnan, J. Peloton, S. Schulze, P. J. Pessi, D. Perley, J. Sollerman, A. Gkini, Y. Hu, T.-W. Chen, E. C. Bellm, T. X. Chen, B. Rusholme
Geophysics
Subsurface Property Mapping using Google AlphaEarth Foundations
Nori Nakata, Jingxiao Liu, Guodong Chen et al. (+2 more)
Subsurface properties are essential for hazard assessment, energy and environmental management, and infrastructure resilience, but direct observations are sparse and uneven, motivating the use of surface observations as indirect constraints. Here we explore whether AlphaEarth
embeddings can be applied to subsurface estimation despite indirect and non-unique physical links between surface and depth. We test this idea in two conterminous U.S. applications: shallow seismic site characterization using $V_S 30$ with
embedding features alone and with conventional
covariates (topographic slope and a tectonic-status indicator), and subsurface temperature reconstruction using
embedding-based nonlinear regression. Across both applications,
embedding-informed models recover spatially coherent, physically plausible patterns and outperform simpler baselines. The comparison also highlights a key difference: domain
covariates materially stabilize $V_S 30$ regression, whereas temperature mapping relies primarily on
embedding features. Overall, the results support the feasibility of foundation-model surface representations for regional surface-to-subsurface inference, while emphasizing the need for robust spatial validation under heterogeneous labels and uneven data coverage.
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All authors Nori Nakata, Jingxiao Liu, Guodong Chen, Rie Nakata, Charuleka Varadharajan
Atomic Physics
Bound-state Compton scattering of linearly polarized photons
Jonas Sommerfeldt, Nick M. Mayer, Anna Maiorova et al. (+4 more)
We present a theoretical study of Compton scattering of X- and $\gamma$-rays by a $K$-shell electron. Special
attention is paid to the double-differential cross section and polarization of the scattered
photons for linearly polarized incident
photons. To investigate these observables, we employ the scattering
matrix (S-
matrix) approach based on relativistic Green's functions. The S-
matrix results are moreover compared with predictions of the free-electron and impulse approximations, allowing us to assess the role of electron binding effects. Detailed calculations are carried out for hydrogen-like Ne$^{9+}$ and Pb$^{81+}$ targets over a wide range of incident
photon energies and scattering angles. The calculations reveal kinematic regimes in which the impulse approximation agrees reasonably well with the S-
matrix results. We also explore the polarization of scattered
photons for slightly depolarized incident radiation, including the highly sensitive case of scattering at $90^\circ$.
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All authors Jonas Sommerfeldt, Nick M. Mayer, Anna Maiorova, Wilko Middents, Stephan Fritzsche, Thomas Stöhlker, Andrey Surzhykov
Optics
Inverse design of exceptional points in a single-resonance two-port network
Y.F. Li, Biao Chen, Y. Wu et al. (+3 more)
Exceptional points (EPs) in non-Hermitian photonic systems enable unconventional control of wave amplitude and phase. However, identifying the EPs in a multidimensional parameter space of a system can be nontrivial and, in some cases, even infeasible. Here we propose an inverse-design method to efficiently locate the scattering EPs for a two-port resonant system supporting a single mode. The proposed method provides a direct way for tuning of
geometric parameters to realize scattering EPs, as confirmed by both full-wave simulation and equivalent
circuit model. In principle, our method is compatible with multi-mode system and applicable to a broad class of resonant systems.
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All authors Y.F. Li, Biao Chen, Y. Wu, Y. Liu, H. Lin, Bin Zhou
Applied Physics
Sensitivity Improvement by Sample Vibration Excitation in Resistivity Measurement for Non-Magnetic Material Using MFM
Kazuma Okamoto, Takumi Imura, Naruto Nakamura et al. (+4 more)
A novel approach for measuring the electrical resistivity of non-
magnetic materials using
magnetic force microscopy (MFM) is discussed. In this method, MFM detects
magnetic fields generated by eddy currents induced by the oscillation of a magnetized probe tip. To enhance measurement sensitivity, it is essential to increase the magnitude of these eddy currents. It is discussed that introducing controlled sample vibration amplifies eddy current generation by increasing the relative velocity between the probe tip and the sample surface. Theoretical analysis predicts increase of the phase shift by sample vibration, and experimental validation using a modified MFM system confirms the improvement in sensitivity. The calculated and experimental results exhibit relatively good agreement, establishing that sample vibration excitation is an effective strategy for high-sensitivity resistivity measurements.
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All authors Kazuma Okamoto, Takumi Imura, Naruto Nakamura, Satoshi Abo, Katsuhisa Murakami, Masayoshi Nagao, Fujio Wakaya
Optics
Time Delay Distribution and Laser Stability in Arbitrary Detuning Asynchronous Optical Sampling
Laura Antonucci, A. Bonvalet, X. Solinas et al. (+1 more)
Arbitrary Detuning ASynchronous OPtical Sampling (ADA-SOPS) is an emerging technique for extending standard pump–probe experiments performed with two femtosecond
lasers to multitimescale experiments, which are of great interest for the study of complex systems. Although no specific requirements are needed for
laser repetition rates, their ratio determines the achievable delay distribution and therefore is strongly related to the temporal resolution of the technique. We report a detailed theoretical analysis of measurement performances with respect to
laser repetition rates, and we validate our model with experimental data. In the case of amplified
laser systems, we demonstrate that achieved delays are inherently correlated to the time interval between amplified pulses, which affects the pulse energy and can generate artifacts. Nevertheless, a deep understanding of the origin of such artifacts allows to suggest several compensation strategies, either during data analysis or at the conception of the experimental setup. Finally we present a new
algorithm integrated into the ADASOPS device: by selecting pairs of probe pulses having the same elapsed time with respect to the previous pulse, it automatically compensates any effect of energy fluctuation.
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All authors Laura Antonucci, A. Bonvalet, X. Solinas, M. Joffre
Atomic Physics · Cross-listed from quant-ph
Spectroscopic measurement of the Casimir-Polder force in the intermediate regime
K. Ton, G. Kestler, D. A. Steck et al. (+1 more)
The Casimir-Polder (CP) effect – the force between a neutral atom and an uncharged conducting plate in empty space – is an intriguing consequence of
quantum vacuum fluctuations. The typically attractive CP potential crosses over from a scaling of $z^{-3}$ at short separations to $z^{-4}$ at long distances, where retardation effects due to the finite speed of light become important. At intermediate distances, where the atom–surface separation is of the order of the wavelength of the dominant atomic transition, experiments have so far relied on indirect methods, such as diffraction or
quantum reflection, to observe the CP effect. Here, we directly reveal the CP force between strontium atoms and a dielectric surface via the induced shifts in the atomic energy levels in the intermediate regime. We spectroscopically probe the CP-induced kHz-frequency shift of ultracold atoms confined by a magic-wavelength optical
lattice at 189(2)~nm from the surface – on the scale of the dominant 461-nm transition. Our measurements agree well with QED calculations and differ from the short-range approximation, while excluding the long-distance one. This paves the way for studying the CP effect across various surface properties and geometries, as well as exploring the tensor nature of the atom-surface potential – all important for the development of hybrid atomic optical-
magnetic quantum devices.
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All authors K. Ton, G. Kestler, D. A. Steck, J. T. Barreiro
Optics
Correlation invariance unlocks robust calibration-free orbital-angular-momentum multiplexing transmission under dynamic scattering scenarios
Haoran Li, Zhiyuan Wang, Zhipeng Yu et al. (+7 more)
Orbital angular momentum (OAM) multiplexing offers a promising approach to high-capacity optical communication by harnessing the orthogonality of
vortex beams. However, its practical deployment is severely limited in real-world settings where dynamic scattering media, such as turbulent atmosphere, distort multiplexed fields into random speckles and disrupt OAM demultiplexing. Although existing wavefront shaping and deep learning methods can mitigate static distortions, they fail under time-varying scattering conditions, leading to significant crosstalk and unreliable recovery. Here, we introduce a new concept, correlation invariance, which enables scattering-immune, robust OAM multiplexed transmission through dynamic media. By capturing orthogonally polarized speckle holograms in a compact common-path geometry and computing their intensity cross-correlation, dynamically imposed scattering phases are cancelled out while deterministic object information is preserved. This allows single-shot reconstruction of both amplitude and phase of the input OAM-multiplexed fields, without any pre-calibration or training. As a proof of principle, we demonstrate high-fidelity transmission of 24-bit RGB data with 99.61% accuracy under static scattering and 98.97% accuracy under dynamic scattering. This approach addresses a long-standing barrier in OAM-based systems and opens avenues for robust high-capacity optical communications, encryption, and imaging in dynamic scattering environments.
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All authors Haoran Li, Zhiyuan Wang, Zhipeng Yu, Xingpeng Du, Tianting Zhong, Jixiong Pu, Ziyang Chen, Vinu R V, Xiangping Li, Puxiang Lai
Data Analysis and Statistics
Development of an LLM-Based System for Automatic Code Generation from HEP Publications
Masahiko Saito, Tomoe Kishimoto and Junichi Tanaka
Ensuring the reproducibility of physics results is one of the crucial challenges in high-energy physics (HEP). In this study, we develop a proof-of-concept system that uses large
language models (LLMs) to extract analysis procedures from HEP publications and generate executable analysis code for reproducing published results. Our method consists of two stages. In the first stage, open-weight LLMs extract event selection criteria, object definitions, and other relevant analysis information from a target paper and, when necessary, from its referenced publications, and then produce a structured selection list. In the second stage, the structured selection list is used to generate analysis code, which is then executed and validated iteratively. As a
benchmark, we use the ATLAS $H \to ZZ^{*} \to 4\ell$ analysis based on
proton-
proton collision data recorded in 2015 and 2016 and released as ATLAS Open Data. This
benchmark allows direct comparison between the generated results and the published analysis, as well as comparison with a manually developed baseline implementation. We separately evaluate selection extraction and code generation in order to clarify the current capabilities and limitations of open-weight LLMs for HEP analysis reproduction. Our initial results show that recent open-weight models can recover many documented selection criteria from papers and references, and that in some runs they can generate event selections fully matching a baseline implementation at the event level. At the same time, stochasticity,
hallucination, and execution failure remain significant challenges. These results suggest that LLMs are already promising as human-in-the-loop tools for reproducibility support, although they are not yet reliable as fully autonomous HEP analysis
agents. In this paper, we report the design of the prototype system and its initial performance evaluation.
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Optics
Observation of Restored Adiabatic State Transfer in Time-Modulated Non-Hermitian Systems
Xiaowei Wang, Ievgen I. Arkhipov, Quan Lin et al. (+5 more)
Exceptional points (EPs) have attracted extensive research interest due to their intriguing properties. One of the hallmarks of EP physics is that dynamically encircling the EPs induces
chiral mode switching, arising from the breakdown of adiabaticity due to the presence of a complex spectrum in the system's
Hamiltonian. While such
chiral mode behavior has been widely observed experimentally, achieving truly adiabatic, and thus symmetric, state transfer, regardless of the winding direction, in time-modulated non-Hermitian systems has remained elusive. In this work, we demonstrate that this long-sought adiabatic state dynamics can indeed be restored. By steering a two-mode photonic setup along specifically designed trajectories in parameter space, we realize conditions where the associated non-Hermitian
evolution operator acquires a purely real spectrum. Moreover, our experimental platform enables controlled switching between symmetric (adiabatic) and
chiral (non-adiabatic) state-transfer regimes for the same set of initial modes, thus effectively implementing a universal symmetric-asymmetric two-mode switch. Our results therefore open new avenues for harnessing unique topological spectral properties of non-Hermitian systems, paving the way for the practical design of versatile optical wave-manipulation devices and for advancing both classical and
quantum information technologies.
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All authors Xiaowei Wang, Ievgen I. Arkhipov, Quan Lin, Huixia Gao, Dengke Qu, Lei Xiao, Franco Nori, Peng Xue
Optics
Scaling Photonic Tensor Cores with Unary and Homodyne Designs
Oluwaseun Alo and Ishan Thakkar
We analyze five photonic microring tensor core designs with a common optical power model. The results show that
circuit ordering, unary encoding, and homodyne accumulation shape scalability, with the last two offering the strongest path to higher parallelism.
Computational Physics · Cross-listed from cs.AI
El Agente Forjador: Task-Driven Agent Generation for Quantum Simulation
Zijian Zhang, Aiwei Yin, Amaan Baweja et al. (+4 more)
AI for science promises to accelerate the discovery process. The advent of large
language models (LLMs) and agentic workflows enables the expediting of a growing range of scientific tasks. However, most of the current generation of agentic systems depend on static, hand-curated toolsets that hinder adaptation to new domains and evolving libraries. We present El Agente Forjador, a multi-
agent framework in which universal coding
agents autonomously forge, validate, and reuse computational tools through a four-stage workflow of tool analysis, tool generation, task execution, and iterative solution evaluation. Evaluated across 24 tasks spanning
quantum chemistry and
quantum dynamics on five coding
agent setups, we compare three operating modes: zero-shot generation of tools per task, reuse of a curriculum-built toolset, and direct problem-solving with the coding
agents as the baseline. We find that our tool generation and reuse framework consistently improves accuracy over the baseline. We also show that reusing a toolset built by a stronger coding
agent can reduce API cost and substantially raises the solution quality for weaker coding
agents. Case studies further demonstrate that tools forged for different domains can be combined to solve hybrid tasks. Taken together, these results show that LLM-based
agents can use their scientific knowledge and coding capabilities to autonomously build reusable scientific tools, pointing toward a paradigm in which
agent capabilities are defined by the tasks they are designed to solve rather than by explicitly engineered implementations.
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All authors Zijian Zhang, Aiwei Yin, Amaan Baweja, Jiaru Bai, Ignacio Gustin, Varinia Bernales, Alán Aspuru-Guzik
Optics
Nonlinear chiral response governed by meta-atom rotation
Dhruv Hariharan, Pavel Tonkaev, Felix Ulrich Brikh et al. (+5 more)
Chiral photonics provides powerful routes for controlling the light handedness, yet nonlinear
chiral responses are typically associated with intricate three-dimensional systems. Here, we demonstrate that strong nonlinear chirality can emerge and be precisely tuned in planar metasurfaces. We study free-standing membrane metasurfaces composed of periodic
lattices of tilted
elliptic holes, which preserve out-of-plane mirror
symmetry while breaking all in-plane mirror
symmetries through the in-plane rotation of the meta-atoms. We demonstrate that optical resonances play a decisive role in governing the nonlinear
chiral response, enabling pronounced circular dichroism in third-harmonic generation even when
symmetry is broken only in plane. We experimentally reveal strong nonlinear
chiral response from the metasurfaces and a striking swapping of nonlinear
chiral channels for complementary meta-atom rotation angles. This behaviour arises from the interplay between
lattice symmetry and meta-atom orientation, which controls the
symmetry of the resonant modes and the resulting nonlinear selection rules. Our results establish meta-atom rotation as a powerful mechanism for engineering nonlinear
chiral responses in planar metasurfaces, opening new opportunities for tunable
chiral nonlinear metaphotonics devices.
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All authors Dhruv Hariharan, Pavel Tonkaev, Felix Ulrich Brikh, Brijesh Kumar, Ivan Toftul, Ivan Sinev, Hatice Altug, Yuri Kivshar
Optics
Non-diffracting meronic spin defects of light
Nilo Mata-Cervera, Miguel A. Porras and Yijie Shen
Optical
vortices are singularity lines where the light field intensity vanishes and its phase is undefined. These threads of darkness are adorned by Gauss's law as lines of pure longitudinal polarization where the polarization plane tilts and winds around. We unveil the resulting
spin field as a unique structure which unifies both topological textures and defects, as it includes a point defect of undefined
spin surrounded by a meronic texture which spans half the
spin unit sphere. Moreover, this intricate topological structure of transverse
spin does not spread in propagation, is localized arbitrarily below the wavelength of light and presents highly anisotropic features. Here we describe these hidden
topologies of transverse
spin embedded in simple scalar
vortex beams, highlighting the diversity of topological structures that arise in two different spaces – the
spin unit sphere and the transverse-axial Poincaré sphere – and discuss the underlying aspects behind their subwavelength localization.
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Fluid Dynamics
Field Inversion Symbolic Regression with Embedded Equation Learner for Interpretable Turbulence Model Correction
Li Jiazhe, Wu Chenyu, He Zizhou et al. (+1 more)
An interpretable, physics-consistent
turbulence model correction framework, termed FISR-Equation Learner (EQL), is proposed by
embedding equation learning directly into a Partial Differential Equations (PDE)-constrained field inversion process based on the adjoint method. Unlike conventional two-stage approaches, the correction model is optimized end-to-end in parameter space using an EQL architecture, enabling the direct identification of compact analytical expressions while maintaining consistency with the governing equations. The method is applied to the shear-stress-transport (SST) model and trained on two canonical separated flows, the curved backward-facing step and the NASA hump. The resulting explicit expression significantly reduces separation bubble overprediction and improves reattachment prediction, achieving performance comparable to neural-network-based end-to-end methods while retaining full interpretability. Generalization is demonstrated on unseen configurations, including periodic hills, a surface-mounted cube, and the high-lift NLR7301 airfoil. The model improves separated-flow predictions and stall characteristics without degrading attached boundary-layer performance. Overall, FISR-EQL provides a practical pathway toward optimal yet transparent data-driven
turbulence model correction.
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All authors Li Jiazhe, Wu Chenyu, He Zizhou, Zhang Yufei
Fluid Dynamics
A Discrete Adjoint Gas-Kinetic Scheme for Aerodynamic Shape Optimization in Turbulent Continuum Flows
Hangkong Wu, Yuze Zhu, Yajun Zhu et al. (+1 more)
This study presents an efficient and accurate discrete adjoint gas-kinetic scheme (GKS) for sensitivity analysis and aerodynamic shape optimization in continuum flow regimes. Developed using the backward mode of algorithmic differentiation (AD), the adjoint solver is rigorously verified against a duality-preserving linearized GKS solver generated via forward-mode AD. The robustness and practical effectiveness of the solver are evaluated through three
benchmark cases: the inverse design of turbine blades, lift-to-drag ratio enhancement, and shock-strength reduction for a NACA 0012 airfoil. To capture realistic flow physics, fully turbulent optimizations are conducted using the one-equation Spalart–Allmaras (SA) model. Numerical results demonstrate excellent agreement between the discrete adjoint and linearized solvers, exhibiting matching sensitivity convergence behaviors, identical asymptotic residual decay rates, and negligible discrepancies in final sensitivity predictions. Furthermore, the optimization studies confirm that targeted design objectives are consistently achieved within a limited number of design cycles, highlighting the solver's computational efficiency, accuracy, and suitability for complex aerodynamic geometries.
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All authors Hangkong Wu, Yuze Zhu, Yajun Zhu, Kun Xu
Applied Physics · Cross-listed from cs.LG
Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework
Hyeonsu Lee and Jihoon Jeong
Accurate temperature field prediction in metal additive manufacturing (AM) is essential for understanding the process-structure-performance relationship. While prior studies have explored generalization to unseen process conditions, they often require extensive
datasets, costly retraining, or pre-training. Generalization across different materials also remains relatively unexplored due to the challenges posed by distinct material-dependent thermal behaviors. This paper introduces a parametric physics-informed neural network (PINN) framework for generalization across unseen materials without labeled data, retraining, or pre-training. The framework adopts a decoupled parametric PINN architecture that separately encodes material properties and spatiotemporal coordinates, fusing them through conditional modulation to better align with the multiplicative role of material parameters in the governing equation and boundary conditions. Physics-guided output scaling derived from Rosenthal's analytical solution and a hybrid optimization strategy are further incorporated to enhance physical consistency, training stability, and convergence. Experiments with numerical simulations across diverse metal
alloys, including both in-distribution and out-of-distribution cases, demonstrate effective generalizability along with superior training efficiency. Specifically, the proposed framework achieved up to a 64.2% reduction in relative L2 error compared to the non-parametric baseline while surpassing its performance within only 4.4% of the baseline training epochs. Ablation studies clarify each component's contribution and scrutinize the severe training instability prevalent in conventional parametric PINNs. Overall, the proposed framework provides an efficient and scalable material-agnostic solution for temperature field modeling, contributing to more flexible and practical deployment in metal AM.
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Fluid Dynamics
Learning to traverse convective flows at moderate to high Rayleigh numbers
Ao Xu, Hua-Lin Wu, Ben-Rui Xu et al. (+1 more)
We study the
navigation of a self-propelled inertial particle in two-dimensional Rayleigh-Bénard convection at Prandtl number $Pr=0.71$ and
cell aspect ratio $\Gamma=4$ for Rayleigh numbers $Ra$ ranging from $10^7$ to $10^{11}$. A reinforcement-learning (RL) controller selects the propulsive acceleration, subject to an upper bound $\mathcal{A}_{\max}$, to achieve a prescribed horizontal displacement. We find that the success rate increases abruptly with $\mathcal{A}_{\max}$ at moderate $Ra$, whereas at higher $Ra$ the transition becomes more gradual and shifts to larger $\mathcal{A}_{\max}$. Moreover, although the completion time increases with $Ra$, the propulsion energy required for successful traversal decreases. Proper orthogonal decomposition indicates that these performance differences are associated with reorganisation of the carrier flow. At moderate $Ra$, the dominant large-scale circulation partitions the domain through persistent transport barriers, requiring a finite thrust surplus to cross them; at higher $Ra$, energy is distributed across many modes, the barriers fragment and transient plume-assisted pathways emerge. Compared with a constant-heading baseline, the learned policy aligns with local currents and consumes significantly less energy. Lagrangian coherent structure analysis further suggests that the RL
agent tends to cross repelling barriers and surf along attracting pathways. Finally, by mapping these behaviours onto the local Eulerian flow
topology using Voronoi tessellation and the $Q$-criterion, we distil an interpretable, physics-based heuristic strategy that retains robust navigability. These results connect turbulent-flow organisation with autonomous
navigation under bounded actuation.
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All authors Ao Xu, Hua-Lin Wu, Ben-Rui Xu, Heng-Dong Xi
Medical Physics
A wearable electrical hemodynamic imaging ring
Gia-Bao Ha, Lucas Takanori Sanchez Shiromizu, Jaehyeon Song et al. (+12 more)
Continuous ambulatory monitoring of peripheral vascular perfusion could enable earlier detection of vascular dysfunction in individuals with diabetes mellitus and more timely management of cardiovascular disease. Clinical imaging modalities provide high-fidelity vascular information but are impractical for ambulatory use, whereas most wearable devices are limited to single-modality sensing and do not provide imaging. Electrical bioimpedance has the potential to bridge this gap by enabling rapid spatial and temporal imaging while remaining sensitive to hemodynamic changes. Here, we introduce a wearable ring with 8 electrodes and 32-channel bioimpedance sensing for finger blood flow imaging. In 96 healthy participants measured at rest and during autonomic maneuvers, we resolve conductivity images in the digital arteries associated with pulsatile blood flow and train neural network models for continuous cuffless blood pressure waveform estimation. We demonstrate the feasibility of bioimpedance imaging in a ring form factor, supporting its potential for ambulatory cuffless hemodynamic monitoring.
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All authors Gia-Bao Ha, Lucas Takanori Sanchez Shiromizu, Jaehyeon Song, Zhuyun Xie, Henry Crandall, Dinali Assylbek, Alexandra Boyadzhiev, Huanan Zhang, Fernando Guevara Vasquez, Ramakrishna Mukkamala, Michael Widlansky, Shamim Nemati, Jesse Capecelatro, C. Alberto Figueroa, Benjamin Sanchez
Fluid Dynamics
Measurements and modeling of swimming speed dependence on stroke frequency in scyphozoan jellyfish
Noa K. Yoder and John O. Dabiri
Scyphozoan jellyfish exhibit the highest locomotive efficiency in the animal kingdom making them of particular interest in fluid dynamics and bioinspired robotics. Despite this prevalent analytical models of jellyfish swimming have been based on the swimming traits of hydrozoan jellyfish which utilize jet propulsion, rather than scyphozoan jellyfish which utilize paddling propulsive methods. Additionally, while stroke frequency is a driving variable in speeds achieved by undulatory swimmers, a similar dependence has not been previously explored for jellyfish. This work investigates the relationship between stroke frequency and swimming speeds in two species of scyphozoan jellyfish, Aurelia aurita and Cassiopea xamachana. An experimental study was conducted using a biohybrid technique that controls the muscle contraction frequency of freely swimming, live jellyfish with portable, implanted microelectronics. Swimming speeds were measured from
video recordings in a 2.4 m tall water tank. It was found that despite differences in their natural swimming frequencies, the Aurelia and Cassiopea displayed similar speed-frequency relationships with peak swimming speeds occurring at 0.55 +/- 0.05 Hz and 0.50 +/- 0.05 Hz respectively. The difference in natural stroke frequency displayed by scyphomedusea despite the shared relationship between swimming speed and stroke frequency in these two species, suggests that natural stroke frequency may be more related to other functions such as filter feeding, rather than locomotion. A new analytical model developed for scyphozoan, paddling jellyfish was shown to have closer agreement with the experimental results than existing models based on jet propulsion. The model demonstrated the driving factors in the relationship between swimming speed and stroke frequency to be the speed of the jellyfish bell margin and changes in body kinematics with stroke frequency.
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